package com.atguigu.day02;

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;

public class Example1 {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        //设置全局并行的为1
        //如果某个算子没有设置并行度，那么这个算子的并行度是1
        env.setParallelism(1);

        env
                .socketTextStream("hadoop102", 9999)
                .setParallelism(1) //表示source算子必须只能占用1个任务插槽
                .flatMap(new FlatMapFunction<String, Tuple2<String,Integer>>() {
                    @Override
                    public void flatMap(String value, Collector<Tuple2<String, Integer>> out) throws Exception {
                        String[] arr = value.split(" ");
                        for (String word : arr) {
                            out.collect(Tuple2.of(word,1));
                        }

                    }
                })
                .setParallelism(2) //表示faltMap算子必须要占用两个任务插槽
                .keyBy(new KeySelector<Tuple2<String, Integer>, String>() {
                    @Override
                    public String getKey(Tuple2<String, Integer> value) throws Exception {
                        return value.f0;
                    }
                })
                .sum("f1")
                .setParallelism(4) // 表示sum算子必须占用4个任务插槽
                .print()
                .setParallelism(1); // 表示sum算子必须占用1个任务插槽




        env.execute();


    }
}
